Real Estate App with Machine Learning
Note: Not all applications have been deployed. For these projects, I have provided links either to the source code repository or to the final deployed version, where available.
About The Project
A Machine Learning project with a frontend interface, enabling users to browse real estate listings and leverage advanced predictive features. Users can: Receive information on whether a property’s price is a good deal, based on its characteristics. Make price predictions during listing creation, using selected property features. Get recommendations for the most similar properties after selecting a specific listing. The models (LR, KNN) were built using scikit-learn, then saved and exposed via an API for real-time predictions. The frontend was developed with Next.js and TailwindCSS, fully integrated with a Supabase database, providing a responsive and intuitive user interface and a smooth, interactive experience.
Technologies used:
Machine Learning
scikit-learn
NumPy
Pandas
NextJS
TailwindCSS
Supabase
Key Features
Price prediction
Deal classifier
Recommendation system powered by ML